Seminar by Dr. Stephen Wu

Uncertainty quantification improves modeling and predictions under measurement noise, inadequate models and computational errors in various science and engineering problems. In this presentation, I show examples from molecular dynamics, pharmacokinetics, seismology and earthquake engineering. I will discuss model calibration and model selection based on a Bayesian framework. I will focus on four important issues when using the Bayesian framework: (1) uncertainty quantification with heterogeneous data or data with a hierarchical structure, (2) solutions to the high computational demand in evidence calculations, (3) uncertainty quantification for real-time predictions, and (4) estimation of rare-event probability. I will demonstrate the applications of various Monte Carlo methods, such as, Particle Filter, Transitional Markov Chain Monte Carlo (similar to Annealed Importance Sampling) and Subset Simulation (aka Splitting Method).